Pattern preview · 12 of 4,089 sample rules shown · site-specific intelligence stays private
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gpu_memory_management
infrastructure-gpu-memory-managemen-after-upgrading-to-vllm-0-6-4-or-later-the-gpu-mem-49b66260
IFAfter upgrading to vLLM 0.6.4 or later, the `gpu_memory_utilization` setting causes allocation failures when multiple vLLM models share the same GPU.
THENDowngrade to vLLM 0.6.3 to restore the previous per-process memory accounting behavior. Alternatively, isolate each model on separate GPU devices using the `CUDA_VISIBLE_DEVICES` environment variable. If you must run multiple models on the same GPU, manually set `gpu_memory_utilization` fractions for each model that sum to no more than 1.0 (e.g., 0.3, 0.7, 1.0 for three models), but be aware this workaround is fragile and will break on restarts or crashes of any model.
Tier 170%
gpu_memory_management
infrastructure-gpu-memory-managemen-vllm-version-0-2-5-or-later-incorrectly-attributes-a72cfd7e
IFvLLM version 0.2.5 or later incorrectly attributes GPU memory occupied by other processes to the current instance, causing the 'No available memory for the cache blocks' error even when free memory exists.
THENApply the fix from PR #2249 or upgrade to a vLLM version that includes it. As a temporary workaround, disable CUDA graphs by passing the `--enforce-eager` flag to the vLLM server, which reduces memory overhead. Also ensure no other GPU-intensive processes are running, and consider lowering `gpu_memory_utilization` if necessary, though this may not fully resolve the profiling issue.
Tier 170%
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